Flexible Sampling for Long-tailed Skin Lesion Classification

• URL: http://arxiv.org/abs/2204.03161v1
• Date: Thu, 7 Apr 2022 02:13:56 GMT
• Title: Flexible Sampling for Long-tailed Skin Lesion Classification
• Authors: Lie Ju, Yicheng Wu, Lin Wang, Zhen Yu, Xin Zhao, Xin Wang, Paul Bonnington, Zongyuan Ge
• Abstract summary: Existing long-tailed learning methods treat each class equally to re-balance the long-tailed distribution. We propose a curriculum learning-based framework called Flexible Sampling for the long-tailed skin lesion classification task.
• Score: 21.790337883680756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most of the medical tasks naturally exhibit a long-tailed distribution due to the complex patient-level conditions and the existence of rare diseases. Existing long-tailed learning methods usually treat each class equally to re-balance the long-tailed distribution. However, considering that some challenging classes may present diverse intra-class distributions, re-balancing all classes equally may lead to a significant performance drop. To address this, in this paper, we propose a curriculum learning-based framework called Flexible Sampling for the long-tailed skin lesion classification task. Specifically, we initially sample a subset of training data as anchor points based on the individual class prototypes. Then, these anchor points are used to pre-train an inference model to evaluate the per-class learning difficulty. Finally, we use a curriculum sampling module to dynamically query new samples from the rest training samples with the learning difficulty-aware sampling probability. We evaluated our model against several state-of-the-art methods on the ISIC dataset. The results with two long-tailed settings have demonstrated the superiority of our proposed training strategy, which achieves a new benchmark for long-tailed skin lesion classification.

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